Investment in shares is one of many kinds of efforts which is sensitive to losses and profits. Due to its speculative nature, the prospects of profit will depend very much on luck factor. One way to suppress the risk and maximize profit is by share portfolio. Share portfolio classifies capitals into shares based on the profit previously earned. In order to avoid big losses, investors must be keen in choosing the right shares and the proportion of shares. This can be done by a search process to arrive at an optimal result starting with an estimate and rechecking before the decision is made. Genetic Algorithm is a biologically inspired stochastic and heuristics maximization process that randomly selects two potential solutions from a population of potential solutions. The process of Genetic Algorithm is based on the natural selection mechanism and biological genetics to determine the structure of high quality individuals in one population. Each problem solving by Genetic Algorithm should be efficient and effective, especially in making chromosome representation to the problem and also in determining the code type related to the choice of elementary operator, that is reproduction, crossover and mutation. Determination of a good fitness function will have an effect on the solution to be obtained.

Investment in shares is one of many kinds of efforts which is sensitive to losses and profits. Due to its speculative nature, the prospects of profit will depend very much on luck factor. One way to suppress the risk and maximize profit is by share portfolio. Share portfolio classifies capitals into shares based on the profit previously earned. In order to avoid big losses, investors must be keen in choosing the right shares and the proportion of shares. This can be done by a search process to arrive at an optimal result starting with an estimate and rechecking before the decision is made. Genetic Algorithm is a biologically inspired stochastic and heuristics maximization process that randomly selects two potential solutions from a population of potential solutions. The process of Genetic Algorithm is based on the natural selection mechanism and biological genetics to determine the structure of high quality individuals in one population. Each problem solving by Genetic Algorithm should be efficient and effective, especially in making chromosome representation to the problem and also in determining the code type related to the choice of elementary operator, that is reproduction, crossover and mutation. Determination of a good fitness function will have an effect on the solution to be obtained.

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